{"title":"Automatiserad iOS-apptestning","slug":"automated-testing-ios","category":"guides","summary":"Använd Synapse + Computer Control API för att automatisera iOS-apptestning via Simulator.","audience":["human","llm"],"tags":["guide","ios","testing","automation"],"difficulty":"advanced","updated":"2026-06-27","word_count":275,"read_minutes":1,"lang":"sv","translated":true,"requested_lang":"sv","content_markdown":"\n# Automatiserad iOS-apptestning\n\nKombinera Synapses minnessystem med Computer Control API för att bygga\nLLM-drivna iOS-apptester. LLM:en minns testscenarier, lär sig av tidigare\nfel och anpassar sig till UI-ändringar.\n\n## Arkitektur\n\n```\n┌──────────────┐ commands ┌──────────────┐ screenshots ┌──────────────┐\n│ LLM Agent │ ─────────────▶│ Synapse │ ────────────────▶ │ iOS Sim │\n│ (Claude) │ │ Computer │ ◀──────────────── │ (via agent) │\n└──────────────┘ │ Control │ results └──────────────┘\n │ └──────────────┘\n │ store/recall\n ▼\n┌──────────────┐\n│ Memories │ (test scenarios, past failures, UI patterns)\n└──────────────┘\n```\n\n## Förutsättningar\n\n- Synapse-konto + Mind Key\n- Synapse MCP-server konfigurerad i Claude Desktop\n- iOS Simulator med `screen-remote-agent` installerad\n- Dator registrerad i Synapse (se [Computer Control API](/docs/api/computers))\n\n## Steg 1: Registrera Simulator-datorn\n\nPå den Mac som kör iOS Simulator:\n\n```bash\n# Get install code from Synapse\ncurl -X POST https://synapse.schaefer.zone/computers/install-code \\\n -H \"Authorization: Bearer YOUR_MIND_KEY\" \\\n -d '{\"computer_name\":\"ios-sim\"}'\n# → { \"install_code\": \"ic_...\" }\n\n# Run screen-remote-agent on the Mac\n# (uses the install code to register)\n```\n\n## Steg 2: Lagra testscenarier i minne\n\nLagra återanvändbara testscenarier som minnen:\n\n```python\nimport requests\n\ndef store_test_scenario(name, steps, app):\n requests.post(f\"{URL}/memory\",\n headers={\"Authorization\": f\"Bearer {MIND_KEY}\"},\n json={\n \"category\": \"skill\",\n \"key\": f\"test_scenario_{name}\",\n \"content\": f\"App: {app}\\nSteps:\\n\" + \"\\n\".join(steps),\n \"tags\": [\"test\", \"ios\", app],\n \"priority\": \"high\"\n })\n\nstore_test_scenario(\"login_flow\", [\n \"Launch app\",\n \"Tap email field\",\n \"Type test@example.com\",\n \"Tap password field\",\n \"Type password123\",\n \"Tap Login button\",\n \"Verify home screen appears\"\n], \"MyApp\")\n```\n\n## Steg 3: LLM-driven testkörning\n\nI Claude Desktop (med Synapse MCP konfigurerad):\n\n```\nRun the login_flow test scenario on the iOS Simulator.\nTake a screenshot after each step and verify the expected UI.\nIf any step fails, store the failure as a memory so we can\navoid it next time.\n```\n\nClaude kommer att:\n\n1. Anropa `memory_search` för att hitta minnet `test_scenario_login_flow`\n2. Anropa `computer_screenshot` för att se aktuellt tillstånd\n3. Utföra varje steg via `computer_command_queue` (klick, skriv)\n4. Verifiera resultat via skärmdumpar\n5. Lagra eventuella fel som `mistake`-minnen\n\n## Steg 4: Självläkande tester\n\nNär ett test misslyckas, lagra felet och återställningen:\n\n```python\ndef store_test_failure(scenario, step, error, recovery):\n requests.post(f\"{URL}/memory\",\n headers={\"Authorization\": f\"Bearer {MIND_KEY}\"},\n json={\n \"category\": \"mistake\",\n \"key\": f\"failure_{scenario}_{step}\",\n \"content\": f\"Scenario: {scenario}\\nStep: {step}\\nError: {error}\\nRecovery: {recovery}\",\n \"tags\": [\"test\", \"failure\", \"ios\", scenario],\n \"priority\": \"high\"\n })\n\n# Example\nstore_test_failure(\"login_flow\", \"tap_login\",\n \"Login button not found at expected coordinates\",\n \"Button moved due to new logo. Search by accessibility label instead.\")\n```\n\nNästa gång LLM:en kör testet återkallar den felet och tillämpar\nåterställningen automatiskt.\n\n## Steg 5: Spårning av testresultat\n\nSpåra testkörningar som uppgifter:\n\n```python\ndef track_test_run(scenario, status, duration):\n requests.post(f\"{URL}/mind/task\",\n headers={\"Authorization\": f\"Bearer {MIND_KEY}\",\n \"Content-Type\": \"application/json\"},\n json={\n \"title\": f\"Test: {scenario}\",\n \"description\": f\"Status: {status}, Duration: {duration}s\",\n \"priority\": \"normal\"\n })\n```\n\n## Vanliga kommandon\n\n| Åtgärd | Kommando |\n|--------|---------|\n| Starta Simulator | `xcrun simctl launch booted com.example.app` |\n| Skärmdump | `computer_screenshot` (via Synapse MCP) |\n| Tryck på (x,y) | `computer_command_queue {type:\"click\", payload:{x,y}}` |\n| Skriv text | `computer_command_queue {type:\"type\", payload:{text:\"...\"}}` |\n| Tryck Home | `computer_command_queue {type:\"key\", payload:{keys:[\"Cmd\",\"Shift\",\"H\"]}}` |\n\n## Bästa praxis\n\n> [!TIP]\n> - **Lagra UI-koordinater som minnen** — UI ändras, men LLM:en kan lära om\n> - **Använd tillgänglighetsetiketter** — mer stabilt än koordinater\n> - **Lagra testdata separat** — använd variabler för användarnamn, lösenord\n> - **Kör tester i rent tillstånd** — återställ Simulator mellan körningar\n> - **Logga skärmdumpar för fel** — användbart för felsökning\n\n## Nästa steg\n\n- [Självläkande tester](/docs/guides/self-healing-tests)\n- [Computer Control API](/docs/api/computers)\n- [Minnesbästa praxis](/docs/guides/memory-best-practices)\n","content_html":"

Automatiserad iOS-apptestning

\n

Kombinera Synapses minnessystem med Computer Control API för att bygga\nLLM-drivna iOS-apptester. LLM:en minns testscenarier, lär sig av tidigare\nfel och anpassar sig till UI-ändringar.

\n

Arkitektur

\n
┌──────────────┐    commands    ┌──────────────┐    screenshots    ┌──────────────┐\n│  LLM Agent   │ ─────────────▶│  Synapse     │ ────────────────▶ │  iOS Sim     │\n│  (Claude)    │               │  Computer    │ ◀──────────────── │  (via agent) │\n└──────────────┘               │  Control     │    results        └──────────────┘\n       │                       └──────────────┘\n       │ store/recall\n       ▼\n┌──────────────┐\n│  Memories    │ (test scenarios, past failures, UI patterns)\n└──────────────┘

Förutsättningar

\n\n

Steg 1: Registrera Simulator-datorn

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På den Mac som kör iOS Simulator:

\n
# Get install code from Synapse\ncurl -X POST https://synapse.schaefer.zone/computers/install-code \\\n  -H "Authorization: Bearer YOUR_MIND_KEY" \\\n  -d '{"computer_name":"ios-sim"}'\n# → { "install_code": "ic_..." }\n\n# Run screen-remote-agent on the Mac\n# (uses the install code to register)

Steg 2: Lagra testscenarier i minne

\n

Lagra återanvändbara testscenarier som minnen:

\n
import requests\n\ndef store_test_scenario(name, steps, app):\n    requests.post(f"{URL}/memory",\n        headers={"Authorization": f"Bearer {MIND_KEY}"},\n        json={\n            "category": "skill",\n            "key": f"test_scenario_{name}",\n            "content": f"App: {app}\\nSteps:\\n" + "\\n".join(steps),\n            "tags": ["test", "ios", app],\n            "priority": "high"\n        })\n\nstore_test_scenario("login_flow", [\n    "Launch app",\n    "Tap email field",\n    "Type test@example.com",\n    "Tap password field",\n    "Type password123",\n    "Tap Login button",\n    "Verify home screen appears"\n], "MyApp")

Steg 3: LLM-driven testkörning

\n

I Claude Desktop (med Synapse MCP konfigurerad):

\n
Run the login_flow test scenario on the iOS Simulator.\nTake a screenshot after each step and verify the expected UI.\nIf any step fails, store the failure as a memory so we can\navoid it next time.

Claude kommer att:

\n
    \n
  1. Anropa memory_search för att hitta minnet test_scenario_login_flow
    2. \n
  2. Anropa computer_screenshot för att se aktuellt tillstånd
    3. \n
  3. Utföra varje steg via computer_command_queue (klick, skriv)
    4. \n
  4. Verifiera resultat via skärmdumpar
    5. \n
  5. Lagra eventuella fel som mistake-minnen
    6. \n
\n

Steg 4: Självläkande tester

\n

När ett test misslyckas, lagra felet och återställningen:

\n
def store_test_failure(scenario, step, error, recovery):\n    requests.post(f"{URL}/memory",\n        headers={"Authorization": f"Bearer {MIND_KEY}"},\n        json={\n            "category": "mistake",\n            "key": f"failure_{scenario}_{step}",\n            "content": f"Scenario: {scenario}\\nStep: {step}\\nError: {error}\\nRecovery: {recovery}",\n            "tags": ["test", "failure", "ios", scenario],\n            "priority": "high"\n        })\n\n# Example\nstore_test_failure("login_flow", "tap_login",\n    "Login button not found at expected coordinates",\n    "Button moved due to new logo. Search by accessibility label instead.")

Nästa gång LLM:en kör testet återkallar den felet och tillämpar\nåterställningen automatiskt.

\n

Steg 5: Spårning av testresultat

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Spåra testkörningar som uppgifter:

\n
def track_test_run(scenario, status, duration):\n    requests.post(f"{URL}/mind/task",\n        headers={"Authorization": f"Bearer {MIND_KEY}",\n                 "Content-Type": "application/json"},\n        json={\n            "title": f"Test: {scenario}",\n            "description": f"Status: {status}, Duration: {duration}s",\n            "priority": "normal"\n        })

Vanliga kommandon

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
ÅtgärdKommando
Starta Simulatorxcrun simctl launch booted com.example.app
Skärmdumpcomputer_screenshot (via Synapse MCP)
Tryck på (x,y)computer_command_queue {type:"click", payload:{x,y}}
Skriv textcomputer_command_queue {type:"type", payload:{text:"..."}}
Tryck Homecomputer_command_queue {type:"key", payload:{keys:["Cmd","Shift","H"]}}
\n

Bästa praxis

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Nästa steg

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